
"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
							import torch
@is_pipeline_test
class 		SCREAMING_SNAKE_CASE_					(		unittest.TestCase					):




		"""simple docstring"""





		__lowercase				:    List[str]		  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		__lowercase				:    Optional[int]		  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					TextaTextGenerationPipeline(model=UpperCamelCase_				,    tokenizer=UpperCamelCase_)
				return generator, ["Something to write", "Something else"]




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					generator("""Something there""")
				self.assertEqual(UpperCamelCase_				,    [{"""generated_text""": ANY(UpperCamelCase_)}])
				# These are encoder decoder, they don't just append to incoming string
				self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""))

				__SCREAMING_SNAKE_CASE				=					generator(["""This is great !""", """Something else"""]				,    num_return_sequences=2				,    do_sample=UpperCamelCase_)
				self.assertEqual(
				    UpperCamelCase_				,    [
				        [{"""generated_text""": ANY(UpperCamelCase_)}, {"""generated_text""": ANY(UpperCamelCase_)}],
				        [{"""generated_text""": ANY(UpperCamelCase_)}, {"""generated_text""": ANY(UpperCamelCase_)}],
				    ]				,    )

				__SCREAMING_SNAKE_CASE				=					generator(
				    ["""This is great !""", """Something else"""]				,    num_return_sequences=2				,    batch_size=2				,    do_sample=UpperCamelCase_)
				self.assertEqual(
				    UpperCamelCase_				,    [
				        [{"""generated_text""": ANY(UpperCamelCase_)}, {"""generated_text""": ANY(UpperCamelCase_)}],
				        [{"""generated_text""": ANY(UpperCamelCase_)}, {"""generated_text""": ANY(UpperCamelCase_)}],
				    ]				,    )

				with self.assertRaises(UpperCamelCase_):
						generator(4)




		@require_torch
		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					pipeline("""text2text-generation"""				,    model="""patrickvonplaten/t5-tiny-random"""				,    framework="""pt""")
				# do_sample=False necessary for reproducibility
				__SCREAMING_SNAKE_CASE				=					generator("""Something there"""				,    do_sample=UpperCamelCase_)
				self.assertEqual(UpperCamelCase_				,    [{"""generated_text""": """"""}])

				__SCREAMING_SNAKE_CASE				=					3
				__SCREAMING_SNAKE_CASE				=					generator(
				    """Something there"""				,    num_return_sequences=UpperCamelCase_				,    num_beams=UpperCamelCase_				,    )
				__SCREAMING_SNAKE_CASE				=					[
				    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
				    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
				    {"""generated_text""": """"""},
				]
				self.assertEqual(UpperCamelCase_				,    UpperCamelCase_)

				__SCREAMING_SNAKE_CASE				=					generator("""This is a test"""				,    do_sample=UpperCamelCase_				,    num_return_sequences=2				,    return_tensors=UpperCamelCase_)
				self.assertEqual(
				    UpperCamelCase_				,    [
				        {"""generated_token_ids""": ANY(torch.Tensor)},
				        {"""generated_token_ids""": ANY(torch.Tensor)},
				    ]				,    )
				__SCREAMING_SNAKE_CASE				=					generator.model.config.eos_token_id
				__SCREAMING_SNAKE_CASE				=					"""<pad>"""
				__SCREAMING_SNAKE_CASE				=					generator(
				    ["""This is a test""", """This is a second test"""]				,    do_sample=UpperCamelCase_				,    num_return_sequences=2				,    batch_size=2				,    return_tensors=UpperCamelCase_				,    )
				self.assertEqual(
				    UpperCamelCase_				,    [
				        [
				            {"""generated_token_ids""": ANY(torch.Tensor)},
				            {"""generated_token_ids""": ANY(torch.Tensor)},
				        ],
				        [
				            {"""generated_token_ids""": ANY(torch.Tensor)},
				            {"""generated_token_ids""": ANY(torch.Tensor)},
				        ],
				    ]				,    )




		@require_tf
		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					pipeline("""text2text-generation"""				,    model="""patrickvonplaten/t5-tiny-random"""				,    framework="""tf""")
				# do_sample=False necessary for reproducibility
				__SCREAMING_SNAKE_CASE				=					generator("""Something there"""				,    do_sample=UpperCamelCase_)
				self.assertEqual(UpperCamelCase_				,    [{"""generated_text""": """"""}])












"""simple docstring"""
def 							A						(				__snake_case: int = 3 ,     __snake_case: int = 7 ,     __snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    1

       for current_denominator in range(1 ,     limit + 1  ):
              __magic_name__      				=    current_denominator * numerator // denominator
              if current_denominator % denominator == 0:
                     current_numerator -= 1
              if current_numerator * max_denominator > current_denominator * max_numerator:
                     __magic_name__      				=    current_numerator
                     __magic_name__      				=    current_denominator
       return max_numerator


if __name__ == "__main__":
 print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))